Teaching during COVID-19: Perceptions of nursing faulty
Bibliographic record
Abstract
Objective: The COVID-19 pandemic resulted in a significant academic impact for health professions students. As a practice profession, course content in programs of nursing are provided in both web-based or on-line formats and through face-to-face classroom or clinical modalities. In response to social distancing and stay-at-home policies, all course formats became web-based and on-line. This required faculty to transition their course content, assignment, assessment requirements, office hours, and consultations, to an on-line format. Data collected in this qualitative study aimed to describe this experience, from the perspective of undergraduate and graduate nursing faculty teaching in a small, private Midwestern University.Methods: Study data, guided by semi-structured interviews, were collected using a virtual format from a convenience sample of ten nurse educators. Each interview was performed by the same researcher and analyzed separately, by both researchers using content analyses and qualitative research methods.Results: Content analyses identified alterations in course structural changes, flexibility in completion of course requirements, providing course content in smaller sections, and being available for academic and psychological support was needed. These data described the personal and professional experiences of faculty specific to course, clinical, and instructor availability.Conclusions: The necessary adaptations were readily developed and implemented. While the stress and uncertainty associated with change was apparent, clear, pro-active communication resulted in course completion and the ability to maintain the plan of study. Flexibility and adaptability, characteristics inherent in nurses, provided a framework for the necessary changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".